Overview

Brought to you by YData

Dataset statistics

Number of variables17
Number of observations1203
Missing cells1231
Missing cells (%)6.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory272.5 KiB
Average record size in memory232.0 B

Variable types

Text1
Numeric15
Categorical1

Alerts

Energy Consumer Price Inflation is highly overall correlated with Headline Consumer Price Inflation and 1 other fieldsHigh correlation
Food Consumer Price Inflation is highly overall correlated with GDP deflator Index growth rate and 2 other fieldsHigh correlation
Freedom to make life choices is highly overall correlated with GDP per Capita and 3 other fieldsHigh correlation
GDP deflator Index growth rate is highly overall correlated with Food Consumer Price Inflation and 3 other fieldsHigh correlation
GDP per Capita is highly overall correlated with Freedom to make life choices and 4 other fieldsHigh correlation
Generosity is highly overall correlated with Healthy life expectancy at birth and 1 other fieldsHigh correlation
Headline Consumer Price Inflation is highly overall correlated with Energy Consumer Price Inflation and 4 other fieldsHigh correlation
Healthy life expectancy at birth is highly overall correlated with Freedom to make life choices and 4 other fieldsHigh correlation
Official Core Consumer Price Inflation is highly overall correlated with Food Consumer Price Inflation and 2 other fieldsHigh correlation
Perceptions of corruption is highly overall correlated with Freedom to make life choices and 2 other fieldsHigh correlation
Producer Price Inflation is highly overall correlated with Energy Consumer Price Inflation and 2 other fieldsHigh correlation
Rank is highly overall correlated with GDP per Capita and 2 other fieldsHigh correlation
Score is highly overall correlated with GDP per Capita and 2 other fieldsHigh correlation
Social support is highly overall correlated with Rank and 1 other fieldsHigh correlation
Year is highly overall correlated with Freedom to make life choices and 4 other fieldsHigh correlation
Energy Consumer Price Inflation has 129 (10.7%) missing values Missing
Food Consumer Price Inflation has 89 (7.4%) missing values Missing
GDP deflator Index growth rate has 16 (1.3%) missing values Missing
Headline Consumer Price Inflation has 23 (1.9%) missing values Missing
Official Core Consumer Price Inflation has 483 (40.1%) missing values Missing
Producer Price Inflation has 446 (37.1%) missing values Missing
Continent has 44 (3.7%) missing values Missing

Reproduction

Analysis started2025-03-22 20:03:38.351615
Analysis finished2025-03-22 20:04:05.485908
Duration27.13 seconds
Software versionydata-profiling vv4.14.0
Download configurationconfig.json

Variables

Distinct148
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Memory size67.1 KiB
2025-03-23T01:34:05.865086image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Length

Max length24
Median length20
Mean length8.0041563
Min length4

Characters and Unicode

Total characters9629
Distinct characters50
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)0.2%

Sample

1st rowAfghanistan
2nd rowAfghanistan
3rd rowAfghanistan
4th rowAfghanistan
5th rowAfghanistan
ValueCountFrequency (%)
united 27
 
1.9%
republic 21
 
1.5%
south 14
 
1.0%
china 14
 
1.0%
and 13
 
0.9%
israel 9
 
0.6%
bosnia 9
 
0.6%
brazil 9
 
0.6%
bulgaria 9
 
0.6%
faso 9
 
0.6%
Other values (156) 1254
90.3%
2025-03-23T01:34:06.511185image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1580
16.4%
i 895
 
9.3%
n 779
 
8.1%
e 623
 
6.5%
r 554
 
5.8%
o 519
 
5.4%
l 364
 
3.8%
t 338
 
3.5%
u 325
 
3.4%
d 295
 
3.1%
Other values (40) 3357
34.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9629
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1580
16.4%
i 895
 
9.3%
n 779
 
8.1%
e 623
 
6.5%
r 554
 
5.8%
o 519
 
5.4%
l 364
 
3.8%
t 338
 
3.5%
u 325
 
3.4%
d 295
 
3.1%
Other values (40) 3357
34.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9629
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1580
16.4%
i 895
 
9.3%
n 779
 
8.1%
e 623
 
6.5%
r 554
 
5.8%
o 519
 
5.4%
l 364
 
3.8%
t 338
 
3.5%
u 325
 
3.4%
d 295
 
3.1%
Other values (40) 3357
34.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9629
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1580
16.4%
i 895
 
9.3%
n 779
 
8.1%
e 623
 
6.5%
r 554
 
5.8%
o 519
 
5.4%
l 364
 
3.8%
t 338
 
3.5%
u 325
 
3.4%
d 295
 
3.1%
Other values (40) 3357
34.9%

Year
Real number (ℝ)

High correlation 

Distinct9
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.8687
Minimum2015
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2025-03-23T01:34:06.653911image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum2015
5-th percentile2015
Q12017
median2019
Q32021
95-th percentile2023
Maximum2023
Range8
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.5511813
Coefficient of variation (CV)0.0012636688
Kurtosis-1.1902573
Mean2018.8687
Median Absolute Deviation (MAD)2
Skewness0.061088151
Sum2428699
Variance6.5085262
MonotonicityNot monotonic
2025-03-23T01:34:06.773309image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2015 140
11.6%
2017 140
11.6%
2016 139
11.6%
2018 138
11.5%
2019 138
11.5%
2020 137
11.4%
2021 135
11.2%
2023 122
10.1%
2022 114
9.5%
ValueCountFrequency (%)
2015 140
11.6%
2016 139
11.6%
2017 140
11.6%
2018 138
11.5%
2019 138
11.5%
2020 137
11.4%
2021 135
11.2%
2022 114
9.5%
2023 122
10.1%
ValueCountFrequency (%)
2023 122
10.1%
2022 114
9.5%
2021 135
11.2%
2020 137
11.4%
2019 138
11.5%
2018 138
11.5%
2017 140
11.6%
2016 139
11.6%
2015 140
11.6%

Rank
Real number (ℝ)

High correlation 

Distinct158
Distinct (%)13.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.975062
Minimum1
Maximum158
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2025-03-23T01:34:06.926081image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile7
Q135
median72
Q3113
95-th percentile145
Maximum158
Range157
Interquartile range (IQR)78

Descriptive statistics

Standard deviation44.77642
Coefficient of variation (CV)0.60529074
Kurtosis-1.2113136
Mean73.975062
Median Absolute Deviation (MAD)39
Skewness0.10407801
Sum88992
Variance2004.9278
MonotonicityNot monotonic
2025-03-23T01:34:07.088169image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
45 9
 
0.7%
52 9
 
0.7%
1 9
 
0.7%
10 9
 
0.7%
9 9
 
0.7%
11 9
 
0.7%
12 9
 
0.7%
13 9
 
0.7%
34 9
 
0.7%
134 9
 
0.7%
Other values (148) 1113
92.5%
ValueCountFrequency (%)
1 9
0.7%
2 9
0.7%
3 9
0.7%
4 9
0.7%
5 9
0.7%
6 8
0.7%
7 9
0.7%
8 9
0.7%
9 9
0.7%
10 9
0.7%
ValueCountFrequency (%)
158 1
 
0.1%
157 2
 
0.2%
156 2
 
0.2%
155 5
0.4%
154 5
0.4%
153 6
0.5%
152 4
0.3%
151 4
0.3%
150 5
0.4%
149 6
0.5%

Score
Real number (ℝ)

High correlation 

Distinct1073
Distinct (%)89.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5031771
Minimum1.859
Maximum7.842
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2025-03-23T01:34:07.276012image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum1.859
5-th percentile3.5973
Q14.6243
median5.546
Q36.3461499
95-th percentile7.3426
Maximum7.842
Range5.983
Interquartile range (IQR)1.7218499

Descriptive statistics

Standard deviation1.1384024
Coefficient of variation (CV)0.20686276
Kurtosis-0.62640792
Mean5.5031771
Median Absolute Deviation (MAD)0.85290003
Skewness-0.14974847
Sum6620.322
Variance1.29596
MonotonicityNot monotonic
2025-03-23T01:34:07.425113image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.125 4
 
0.3%
6.455 3
 
0.2%
5.199 3
 
0.2%
5.89 3
 
0.2%
5.129 3
 
0.2%
5.192 3
 
0.2%
2.905 3
 
0.2%
5.155 3
 
0.2%
4.35 3
 
0.2%
6.123 3
 
0.2%
Other values (1063) 1172
97.4%
ValueCountFrequency (%)
1.859 1
0.1%
2.392 1
0.1%
2.404 1
0.1%
2.523 1
0.1%
2.566900015 1
0.1%
2.693000078 1
0.1%
2.816600084 1
0.1%
2.839 1
0.1%
2.853 1
0.1%
2.904999971 1
0.1%
ValueCountFrequency (%)
7.842 1
0.1%
7.821 1
0.1%
7.808700085 1
0.1%
7.804 1
0.1%
7.769 1
0.1%
7.645599842 1
0.1%
7.636 1
0.1%
7.632 1
0.1%
7.62 1
0.1%
7.6 1
0.1%

GDP per Capita
Real number (ℝ)

High correlation 

Distinct1155
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7972937
Minimum0
Maximum11.66
Zeros5
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2025-03-23T01:34:08.193893image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.2741
Q10.8005
median1.16492
Q31.704
95-th percentile10.5545
Maximum11.66
Range11.66
Interquartile range (IQR)0.9035

Descriptive statistics

Standard deviation3.5479665
Coefficient of variation (CV)1.2683568
Kurtosis0.24547049
Mean2.7972937
Median Absolute Deviation (MAD)0.40038
Skewness1.4339488
Sum3365.1443
Variance12.588066
MonotonicityNot monotonic
2025-03-23T01:34:08.669726image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5
 
0.4%
0.96 3
 
0.2%
0.332 3
 
0.2%
1.34 3
 
0.2%
1.324 3
 
0.2%
1.043 2
 
0.2%
10.776 2
 
0.2%
0.985 2
 
0.2%
1.362 2
 
0.2%
1.452 2
 
0.2%
Other values (1145) 1176
97.8%
ValueCountFrequency (%)
0 5
0.4%
0.0153 1
 
0.1%
0.01604 1
 
0.1%
0.022643184 1
 
0.1%
0.024 1
 
0.1%
0.026 1
 
0.1%
0.041072082 1
 
0.1%
0.046 1
 
0.1%
0.06831 1
 
0.1%
0.0694 1
 
0.1%
ValueCountFrequency (%)
11.66 1
0.1%
11.647 1
0.1%
11.571 1
0.1%
11.527 1
0.1%
11.488 1
0.1%
11.342 1
0.1%
11.164 1
0.1%
11.145 1
0.1%
11.117 1
0.1%
11.088 1
0.1%

Social support
Real number (ℝ)

High correlation 

Distinct1011
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.98319275
Minimum0
Maximum1.644
Zeros6
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2025-03-23T01:34:09.242128image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.51301195
Q10.799505
median0.934
Q31.2145043
95-th percentile1.497846
Maximum1.644
Range1.644
Interquartile range (IQR)0.41499929

Descriptive statistics

Standard deviation0.30270529
Coefficient of variation (CV)0.30787991
Kurtosis-0.033933682
Mean0.98319275
Median Absolute Deviation (MAD)0.196
Skewness-0.10073856
Sum1182.7809
Variance0.091630494
MonotonicityNot monotonic
2025-03-23T01:34:09.748319image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6
 
0.5%
0.896 5
 
0.4%
0.934 5
 
0.4%
0.943 4
 
0.3%
0.836 4
 
0.3%
0.891 4
 
0.3%
1.232 4
 
0.3%
0.888 4
 
0.3%
0.882 4
 
0.3%
0.983 4
 
0.3%
Other values (1001) 1159
96.3%
ValueCountFrequency (%)
0 6
0.5%
0.064 1
 
0.1%
0.10419 1
 
0.1%
0.11037 1
 
0.1%
0.13995 1
 
0.1%
0.147 1
 
0.1%
0.18519 1
 
0.1%
0.19249 1
 
0.1%
0.23442 1
 
0.1%
0.24749 1
 
0.1%
ValueCountFrequency (%)
1.644 1
0.1%
1.624 1
0.1%
1.610574007 1
0.1%
1.601 1
0.1%
1.592 1
0.1%
1.59 1
0.1%
1.587 1
0.1%
1.584 1
0.1%
1.583 1
0.1%
1.582 2
0.2%

Healthy life expectancy at birth
Real number (ℝ)

High correlation 

Distinct1066
Distinct (%)88.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.676503
Minimum0
Maximum76.953
Zeros5
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2025-03-23T01:34:10.042178image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.21602309
Q10.582975
median0.79256552
Q359.512076
95-th percentile71.990083
Maximum76.953
Range76.953
Interquartile range (IQR)58.929101

Descriptive statistics

Standard deviation30.42178
Coefficient of variation (CV)1.403445
Kurtosis-1.334626
Mean21.676503
Median Absolute Deviation (MAD)0.32556552
Skewness0.7810523
Sum26076.833
Variance925.48467
MonotonicityNot monotonic
2025-03-23T01:34:10.271223image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.999 5
 
0.4%
0.815 5
 
0.4%
0.808 5
 
0.4%
0 5
 
0.4%
0.555 4
 
0.3%
0.828 4
 
0.3%
0.874 4
 
0.3%
67 3
 
0.2%
0.777 3
 
0.2%
0.779 3
 
0.2%
Other values (1056) 1162
96.6%
ValueCountFrequency (%)
0 5
0.4%
0.005564754 1
 
0.1%
0.01 1
 
0.1%
0.018772686 1
 
0.1%
0.03824 1
 
0.1%
0.041134715 1
 
0.1%
0.04776 1
 
0.1%
0.048 1
 
0.1%
0.049868666 1
 
0.1%
0.04991 1
 
0.1%
ValueCountFrequency (%)
76.953 1
0.1%
76.80458069 1
0.1%
75.1 1
0.1%
75.00096893 1
0.1%
74.7 1
0.1%
74.40270996 1
0.1%
74.4 1
0.1%
74.349 1
0.1%
74.10244751 1
0.1%
74 1
0.1%

Freedom to make life choices
Real number (ℝ)

High correlation 

Distinct1010
Distinct (%)84.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.55364892
Minimum0
Maximum0.97499812
Zeros6
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2025-03-23T01:34:10.444697image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.188343
Q10.40548
median0.54604
Q30.729
95-th percentile0.90557909
Maximum0.97499812
Range0.97499812
Interquartile range (IQR)0.32352

Descriptive statistics

Standard deviation0.22110778
Coefficient of variation (CV)0.3993646
Kurtosis-0.60688097
Mean0.55364892
Median Absolute Deviation (MAD)0.15996
Skewness-0.11187636
Sum666.03965
Variance0.04888865
MonotonicityNot monotonic
2025-03-23T01:34:10.609136image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6
 
0.5%
0.877 5
 
0.4%
0.448 4
 
0.3%
0.822 4
 
0.3%
0.677 4
 
0.3%
0.724 4
 
0.3%
0.557 4
 
0.3%
0.498 3
 
0.2%
0.647 3
 
0.2%
0.654 3
 
0.2%
Other values (1000) 1163
96.7%
ValueCountFrequency (%)
0 6
0.5%
0.00589 1
 
0.1%
0.01 1
 
0.1%
0.014995855 1
 
0.1%
0.016 1
 
0.1%
0.025 1
 
0.1%
0.026 1
 
0.1%
0.030369857 1
 
0.1%
0.0432 1
 
0.1%
0.05822 1
 
0.1%
ValueCountFrequency (%)
0.974998116 1
0.1%
0.97 1
0.1%
0.961 1
0.1%
0.96 1
0.1%
0.959704638 1
0.1%
0.959 1
0.1%
0.958 1
0.1%
0.955750287 1
0.1%
0.955 1
0.1%
0.951444268 1
0.1%

Generosity
Real number (ℝ)

High correlation 

Distinct922
Distinct (%)76.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.13906952
Minimum-0.30090737
Maximum0.83807516
Zeros7
Zeros (%)0.6%
Negative216
Negative (%)18.0%
Memory size9.5 KiB
2025-03-23T01:34:10.760521image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-0.30090737
5-th percentile-0.1499
Q10.04563467
median0.14578497
Q30.23585444
95-th percentile0.410521
Maximum0.83807516
Range1.1389825
Interquartile range (IQR)0.19021977

Descriptive statistics

Standard deviation0.16413231
Coefficient of variation (CV)1.1802177
Kurtosis0.63674019
Mean0.13906952
Median Absolute Deviation (MAD)0.094784974
Skewness0.082818577
Sum167.30063
Variance0.026939414
MonotonicityNot monotonic
2025-03-23T01:34:10.925652image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7
 
0.6%
0.089 7
 
0.6%
0.175 7
 
0.6%
0.153 6
 
0.5%
0.134 6
 
0.5%
0.098 5
 
0.4%
0.202 5
 
0.4%
0.032 5
 
0.4%
0.187 5
 
0.4%
0.099 5
 
0.4%
Other values (912) 1145
95.2%
ValueCountFrequency (%)
-0.300907373 1
0.1%
-0.288 1
0.1%
-0.258 1
0.1%
-0.254 1
0.1%
-0.250394493 1
0.1%
-0.246910349 1
0.1%
-0.246 1
0.1%
-0.244 1
0.1%
-0.240377247 1
0.1%
-0.240255281 1
0.1%
ValueCountFrequency (%)
0.838075161 1
0.1%
0.81971 1
0.1%
0.79588 1
0.1%
0.611704588 1
0.1%
0.598 1
0.1%
0.58696 1
0.1%
0.5763 1
0.1%
0.574730575 1
0.1%
0.57212311 1
0.1%
0.566 1
0.1%

Perceptions of corruption
Real number (ℝ)

High correlation 

Distinct937
Distinct (%)78.0%
Missing1
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean0.32602694
Minimum0
Maximum0.939
Zeros7
Zeros (%)0.6%
Negative0
Negative (%)0.0%
Memory size9.5 KiB
2025-03-23T01:34:11.077160image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.025016819
Q10.074265
median0.160825
Q30.66
95-th percentile0.87595
Maximum0.939
Range0.939
Interquartile range (IQR)0.585735

Descriptive statistics

Standard deviation0.31201295
Coefficient of variation (CV)0.95701587
Kurtosis-1.0964897
Mean0.32602694
Median Absolute Deviation (MAD)0.122825
Skewness0.741176
Sum391.88438
Variance0.097352082
MonotonicityNot monotonic
2025-03-23T01:34:11.239928image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.082 9
 
0.7%
0.028 8
 
0.7%
0 7
 
0.6%
0.034 7
 
0.6%
0.064 7
 
0.6%
0.074 6
 
0.5%
0.055 6
 
0.5%
0.056 5
 
0.4%
0.089 5
 
0.4%
0.078 5
 
0.4%
Other values (927) 1137
94.5%
ValueCountFrequency (%)
0 7
0.6%
0.001 1
 
0.1%
0.00227 1
 
0.1%
0.00322 1
 
0.1%
0.004 1
 
0.1%
0.004387901 1
 
0.1%
0.005 2
 
0.2%
0.006 5
0.4%
0.00615 1
 
0.1%
0.00649 1
 
0.1%
ValueCountFrequency (%)
0.939 1
0.1%
0.938 1
0.1%
0.935585141 1
0.1%
0.934300244 1
0.1%
0.933769107 1
0.1%
0.933686554 1
0.1%
0.932 1
0.1%
0.931 1
0.1%
0.929 1
0.1%
0.925 1
0.1%

Energy Consumer Price Inflation
Real number (ℝ)

High correlation  Missing 

Distinct1063
Distinct (%)99.0%
Missing129
Missing (%)10.7%
Infinite0
Infinite (%)0.0%
Mean6.4064402
Minimum-23.879999
Maximum306.43167
Zeros2
Zeros (%)0.2%
Negative205
Negative (%)17.0%
Memory size9.5 KiB
2025-03-23T01:34:11.386804image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-23.879999
5-th percentile-3.5798467
Q10.59668782
median2.736892
Q37.0671115
95-th percentile23.636825
Maximum306.43167
Range330.31167
Interquartile range (IQR)6.4704236

Descriptive statistics

Standard deviation16.686408
Coefficient of variation (CV)2.6046302
Kurtosis117.7548
Mean6.4064402
Median Absolute Deviation (MAD)2.8017084
Skewness8.5684044
Sum6880.5168
Variance278.4362
MonotonicityNot monotonic
2025-03-23T01:34:11.544069image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.28 2
 
0.2%
3.845757598 2
 
0.2%
-9.5 2
 
0.2%
-6.5 2
 
0.2%
-1.409999967 2
 
0.2%
1.649999976 2
 
0.2%
0 2
 
0.2%
2.7 2
 
0.2%
1.08 2
 
0.2%
9.88394558 2
 
0.2%
Other values (1053) 1054
87.6%
(Missing) 129
 
10.7%
ValueCountFrequency (%)
-23.87999916 1
0.1%
-22.05954498 1
0.1%
-20.82351047 1
0.1%
-15.78456276 1
0.1%
-14.8 1
0.1%
-11.89491089 1
0.1%
-10.93382348 1
0.1%
-10.18999958 1
0.1%
-9.901313847 1
0.1%
-9.78123785 1
0.1%
ValueCountFrequency (%)
306.4316731 1
0.1%
180.5260149 1
0.1%
126.9034691 1
0.1%
115.8300018 1
0.1%
115.3806484 1
0.1%
110.8838916 1
0.1%
105.2143218 1
0.1%
97.24654997 1
0.1%
90.80807096 1
0.1%
71.42795733 1
0.1%

Food Consumer Price Inflation
Real number (ℝ)

High correlation  Missing 

Distinct1113
Distinct (%)99.9%
Missing89
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean8.036627
Minimum-22.030001
Maximum601.02024
Zeros1
Zeros (%)0.1%
Negative117
Negative (%)9.7%
Memory size9.5 KiB
2025-03-23T01:34:11.713159image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-22.030001
5-th percentile-1.0877016
Q11.2763305
median3.7140043
Q39.2691211
95-th percentile21.245767
Maximum601.02024
Range623.05024
Interquartile range (IQR)7.9927906

Descriptive statistics

Standard deviation26.342455
Coefficient of variation (CV)3.2777999
Kurtosis270.00062
Mean8.036627
Median Absolute Deviation (MAD)3.0739078
Skewness14.470468
Sum8952.8025
Variance693.92493
MonotonicityNot monotonic
2025-03-23T01:34:11.869319image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3.54 2
 
0.2%
-0.8399999738 1
 
0.1%
1.895358846 1
 
0.1%
2.174084663 1
 
0.1%
-0.6034622192 1
 
0.1%
-0.05836892501 1
 
0.1%
11.86689959 1
 
0.1%
10.74305 1
 
0.1%
-0.207 1
 
0.1%
0.7895888686 1
 
0.1%
Other values (1103) 1103
91.7%
(Missing) 89
 
7.4%
ValueCountFrequency (%)
-22.03000069 1
0.1%
-10.98999977 1
0.1%
-6.433373928 1
0.1%
-5.392762661 1
0.1%
-5.367840767 1
0.1%
-4.698972225 1
0.1%
-4.589584939 1
0.1%
-4.154223442 1
0.1%
-4.15 1
0.1%
-3.796073914 1
0.1%
ValueCountFrequency (%)
601.0202356 1
0.1%
310.68 1
0.1%
276.0752887 1
0.1%
254.5563895 1
0.1%
253.96 1
0.1%
149.9670345 1
0.1%
144.4401488 1
0.1%
105.8 1
0.1%
86.12437052 1
0.1%
85.64528 1
0.1%

GDP deflator Index growth rate
Real number (ℝ)

High correlation  Missing 

Distinct1187
Distinct (%)100.0%
Missing16
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean6.9423095
Minimum-26.1
Maximum812.24746
Zeros0
Zeros (%)0.0%
Negative127
Negative (%)10.6%
Memory size9.5 KiB
2025-03-23T01:34:12.115827image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-26.1
5-th percentile-1.7061326
Q11.3537373
median3.1762093
Q36.9960206
95-th percentile19.111651
Maximum812.24746
Range838.34746
Interquartile range (IQR)5.6422832

Descriptive statistics

Standard deviation31.771016
Coefficient of variation (CV)4.5764333
Kurtosis434.44007
Mean6.9423095
Median Absolute Deviation (MAD)2.2988621
Skewness19.18483
Sum8240.5213
Variance1009.3975
MonotonicityNot monotonic
2025-03-23T01:34:12.279958image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2.665090323 1
 
0.1%
6.477990178 1
 
0.1%
3.328088635 1
 
0.1%
3.704900779 1
 
0.1%
4.668077687 1
 
0.1%
3.59211218 1
 
0.1%
4.123860851 1
 
0.1%
4.58957999 1
 
0.1%
7.588966098 1
 
0.1%
5.344951489 1
 
0.1%
Other values (1177) 1177
97.8%
(Missing) 16
 
1.3%
ValueCountFrequency (%)
-26.1 1
0.1%
-25.9585743 1
0.1%
-25.12943741 1
0.1%
-18.77874769 1
0.1%
-16.90859566 1
0.1%
-15.4791652 1
0.1%
-15.06229768 1
0.1%
-13.40183317 1
0.1%
-12.52085062 1
0.1%
-11.97109726 1
0.1%
ValueCountFrequency (%)
812.2474628 1
0.1%
568.9718616 1
0.1%
259.2788732 1
0.1%
156.3875185 1
0.1%
150.000705 1
0.1%
113.2949806 1
0.1%
113.0184336 1
0.1%
112.264944 1
0.1%
110.4000707 1
0.1%
95.38140897 1
0.1%

Headline Consumer Price Inflation
Real number (ℝ)

High correlation  Missing 

Distinct1160
Distinct (%)98.3%
Missing23
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean7.3926012
Minimum-3.752996
Maximum557.21
Zeros0
Zeros (%)0.0%
Negative95
Negative (%)7.9%
Memory size9.5 KiB
2025-03-23T01:34:12.438042image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-3.752996
5-th percentile-0.501576
Q11.392791
median3.4457291
Q36.7512514
95-th percentile19.408171
Maximum557.21
Range560.963
Interquartile range (IQR)5.3584604

Descriptive statistics

Standard deviation25.370648
Coefficient of variation (CV)3.4318972
Kurtosis243.38748
Mean7.3926012
Median Absolute Deviation (MAD)2.4457291
Skewness13.77759
Sum8723.2694
Variance643.66976
MonotonicityNot monotonic
2025-03-23T01:34:12.602153image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.81 3
 
0.2%
0.63 3
 
0.2%
1 3
 
0.2%
2 3
 
0.2%
4.5 2
 
0.2%
1.07 2
 
0.2%
2.44 2
 
0.2%
1.5 2
 
0.2%
1.95 2
 
0.2%
2.82 2
 
0.2%
Other values (1150) 1156
96.1%
(Missing) 23
 
1.9%
ValueCountFrequency (%)
-3.752995968 1
0.1%
-3.23300004 1
0.1%
-2.894000053 1
0.1%
-2.81 1
0.1%
-2.519999981 1
0.1%
-2.410000086 1
0.1%
-2.317706293 1
0.1%
-2.098478556 1
0.1%
-2.09100008 1
0.1%
-2.079403179 1
0.1%
ValueCountFrequency (%)
557.21 1
0.1%
379.848999 1
0.1%
255.2920074 1
0.1%
221.341644 1
0.1%
189.4364086 1
0.1%
187.867 1
0.1%
154.7586773 1
0.1%
128.4147077 1
0.1%
105.4019823 1
0.1%
104.7051706 1
0.1%

Official Core Consumer Price Inflation
Real number (ℝ)

High correlation  Missing 

Distinct713
Distinct (%)99.0%
Missing483
Missing (%)40.1%
Infinite0
Infinite (%)0.0%
Mean3.5172931
Minimum-28.619415
Maximum58.851863
Zeros0
Zeros (%)0.0%
Negative47
Negative (%)3.9%
Memory size9.5 KiB
2025-03-23T01:34:12.757865image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-28.619415
5-th percentile-0.12683919
Q11.0550302
median2.2468375
Q34.6725305
95-th percentile10.854167
Maximum58.851863
Range87.471278
Interquartile range (IQR)3.6175003

Descriptive statistics

Standard deviation5.5359305
Coefficient of variation (CV)1.5739179
Kurtosis36.565144
Mean3.5172931
Median Absolute Deviation (MAD)1.5868933
Skewness3.7410811
Sum2532.451
Variance30.646526
MonotonicityNot monotonic
2025-03-23T01:34:13.072858image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.3 4
 
0.3%
1.9 2
 
0.2%
1.6 2
 
0.2%
1.5 2
 
0.2%
2.3 2
 
0.2%
3.202304363 1
 
0.1%
4.70108223 1
 
0.1%
12.33791426 1
 
0.1%
8.131546122 1
 
0.1%
2.608965 1
 
0.1%
Other values (703) 703
58.4%
(Missing) 483
40.1%
ValueCountFrequency (%)
-28.61941528 1
0.1%
-24.84578514 1
0.1%
-19.07710648 1
0.1%
-12.82376003 1
0.1%
-12.55230141 1
0.1%
-10.99417591 1
0.1%
-2.60256958 1
0.1%
-2.433150053 1
0.1%
-2.076419115 1
0.1%
-2.02203368 1
0.1%
ValueCountFrequency (%)
58.85186307 1
0.1%
58.61293781 1
0.1%
42.04710672 1
0.1%
40.39288712 1
0.1%
30.96879915 1
0.1%
23.77119637 1
0.1%
22.67944716 1
0.1%
22.30119705 1
0.1%
22.21309853 1
0.1%
20.75616458 1
0.1%

Producer Price Inflation
Real number (ℝ)

High correlation  Missing 

Distinct756
Distinct (%)99.9%
Missing446
Missing (%)37.1%
Infinite0
Infinite (%)0.0%
Mean5.6912134
Minimum-83.339781
Maximum128.47664
Zeros0
Zeros (%)0.0%
Negative210
Negative (%)17.5%
Memory size9.5 KiB
2025-03-23T01:34:13.238830image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Quantile statistics

Minimum-83.339781
5-th percentile-4.9417922
Q1-0.29602324
median2.7292556
Q38.0534053
95-th percentile28.662938
Maximum128.47664
Range211.81642
Interquartile range (IQR)8.3494285

Descriptive statistics

Standard deviation13.384842
Coefficient of variation (CV)2.3518434
Kurtosis24.041912
Mean5.6912134
Median Absolute Deviation (MAD)3.6548428
Skewness2.8178734
Sum4308.2485
Variance179.15401
MonotonicityNot monotonic
2025-03-23T01:34:13.390372image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-0.7567219449 2
 
0.2%
8.643110469 1
 
0.1%
14.17915058 1
 
0.1%
37.75110444 1
 
0.1%
51.88188309 1
 
0.1%
-21.80702733 1
 
0.1%
-22.67300797 1
 
0.1%
-2.519125223 1
 
0.1%
1.896838188 1
 
0.1%
4.870432377 1
 
0.1%
Other values (746) 746
62.0%
(Missing) 446
37.1%
ValueCountFrequency (%)
-83.33978078 1
0.1%
-37.92599106 1
0.1%
-31.43005424 1
0.1%
-30.59172731 1
0.1%
-30.57964516 1
0.1%
-24.79999542 1
0.1%
-22.67300797 1
0.1%
-22.51490211 1
0.1%
-21.80702733 1
0.1%
-20.52078056 1
0.1%
ValueCountFrequency (%)
128.4766376 1
0.1%
128.4736965 1
0.1%
73.29973558 1
0.1%
69.59897033 1
0.1%
61.04710887 1
0.1%
60.27334601 1
0.1%
59.08549504 1
0.1%
58.31078339 1
0.1%
56.13898087 1
0.1%
54.6558146 1
0.1%

Continent
Categorical

Missing 

Distinct6
Distinct (%)0.5%
Missing44
Missing (%)3.7%
Memory size65.6 KiB
Europe
363 
Africa
322 
Asia
261 
North America
112 
South America
83 

Length

Max length13
Median length6
Mean length6.7428818
Min length4

Characters and Unicode

Total characters7815
Distinct characters20
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAsia
2nd rowAsia
3rd rowAsia
4th rowAsia
5th rowAsia

Common Values

ValueCountFrequency (%)
Europe 363
30.2%
Africa 322
26.8%
Asia 261
21.7%
North America 112
 
9.3%
South America 83
 
6.9%
Oceania 18
 
1.5%
(Missing) 44
 
3.7%

Length

2025-03-23T01:34:13.520828image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-03-23T01:34:13.654959image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ValueCountFrequency (%)
europe 363
26.8%
africa 322
23.8%
asia 261
19.3%
america 195
14.4%
north 112
 
8.3%
south 83
 
6.1%
oceania 18
 
1.3%

Most occurring characters

ValueCountFrequency (%)
r 992
12.7%
a 814
10.4%
i 796
10.2%
A 778
10.0%
e 576
 
7.4%
o 558
 
7.1%
c 535
 
6.8%
u 446
 
5.7%
E 363
 
4.6%
p 363
 
4.6%
Other values (10) 1594
20.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7815
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
r 992
12.7%
a 814
10.4%
i 796
10.2%
A 778
10.0%
e 576
 
7.4%
o 558
 
7.1%
c 535
 
6.8%
u 446
 
5.7%
E 363
 
4.6%
p 363
 
4.6%
Other values (10) 1594
20.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7815
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
r 992
12.7%
a 814
10.4%
i 796
10.2%
A 778
10.0%
e 576
 
7.4%
o 558
 
7.1%
c 535
 
6.8%
u 446
 
5.7%
E 363
 
4.6%
p 363
 
4.6%
Other values (10) 1594
20.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7815
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
r 992
12.7%
a 814
10.4%
i 796
10.2%
A 778
10.0%
e 576
 
7.4%
o 558
 
7.1%
c 535
 
6.8%
u 446
 
5.7%
E 363
 
4.6%
p 363
 
4.6%
Other values (10) 1594
20.4%

Interactions

2025-03-23T01:34:02.218101image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:38.997546image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:40.690664image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:42.338459image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:43.929430image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:45.505160image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:47.058726image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:48.788112image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:50.348359image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:51.865633image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:53.414745image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:55.122661image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:56.808689image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:58.411002image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:34:00.337691image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:34:02.343970image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:39.204514image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:40.807880image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:42.444188image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:44.039186image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:45.621910image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:47.183758image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:48.900501image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:50.477183image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:51.969904image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:53.524863image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:55.237928image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2025-03-23T01:33:47.721056image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2025-03-23T01:33:54.322360image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:55.928120image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2025-03-23T01:33:59.228375image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:34:01.187284image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:34:03.478560image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:39.950101image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:41.543406image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:43.165219image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:44.780176image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:46.354369image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:47.933650image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:49.640300image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:51.211326image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:52.708457image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:54.422764image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:56.030312image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:57.714318image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:59.432387image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:34:01.274945image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:34:03.598997image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:40.048773image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:41.644252image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2025-03-23T01:33:56.149317image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2025-03-23T01:33:59.703350image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:34:01.384807image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:34:03.720431image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:40.157040image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:41.775522image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:43.368751image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:44.983205image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:46.558228image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:48.154235image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:49.843159image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:51.404784image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:52.892059image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:54.621777image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:56.258440image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:57.905565image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:59.808227image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:34:01.492895image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:34:03.846421image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2025-03-23T01:33:51.508200image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:53.006252image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:54.725222image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:56.377737image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:58.013857image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:59.913634image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:34:01.689290image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:34:03.963820image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:40.366477image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:42.021249image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:43.557815image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:45.183344image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2025-03-23T01:33:48.374735image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2025-03-23T01:33:51.597724image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:53.116983image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:54.821521image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:56.487767image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:58.103429image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:34:00.012742image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:34:01.852736image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:34:04.125467image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2025-03-23T01:33:48.480304image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:50.143887image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2025-03-23T01:33:53.218978image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:54.927497image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
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2025-03-23T01:34:00.123423image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:34:01.989303image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:34:04.228972image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:40.592242image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:42.247004image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:43.847887image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:45.406861image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:46.950864image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:48.581599image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:50.246713image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:51.782079image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:53.312930image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:55.023787image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:56.698374image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:33:58.315728image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:34:00.233997image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
2025-03-23T01:34:02.109899image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/

Correlations

2025-03-23T01:34:13.803427image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
ContinentEnergy Consumer Price InflationFood Consumer Price InflationFreedom to make life choicesGDP deflator Index growth rateGDP per CapitaGenerosityHeadline Consumer Price InflationHealthy life expectancy at birthOfficial Core Consumer Price InflationPerceptions of corruptionProducer Price InflationRankScoreSocial supportYear
Continent1.0000.0690.0750.1930.0000.2740.1430.0000.2260.0960.1950.0960.3790.3800.2260.000
Energy Consumer Price Inflation0.0691.0000.446-0.0300.4860.0480.0060.648-0.1470.483-0.0030.5880.144-0.144-0.0640.225
Food Consumer Price Inflation0.0750.4461.0000.0920.6190.033-0.1280.860-0.0540.7440.1240.4790.219-0.215-0.2350.368
Freedom to make life choices0.193-0.0300.0921.0000.0340.575-0.3560.0170.7630.0700.7370.075-0.3890.397-0.1220.601
GDP deflator Index growth rate0.0000.4860.6190.0341.0000.054-0.0720.698-0.0750.6550.0650.6500.175-0.173-0.1250.272
GDP per Capita0.2740.0480.0330.5750.0541.000-0.338-0.0120.6870.0780.4620.187-0.5520.5500.1070.568
Generosity0.1430.006-0.128-0.356-0.072-0.3381.000-0.077-0.519-0.152-0.421-0.136-0.0500.0450.291-0.521
Headline Consumer Price Inflation0.0000.6480.8600.0170.698-0.012-0.0771.000-0.1550.8780.0620.6500.272-0.270-0.2150.327
Healthy life expectancy at birth0.226-0.147-0.0540.763-0.0750.687-0.519-0.1551.000-0.1470.685-0.142-0.4650.469-0.0470.552
Official Core Consumer Price Inflation0.0960.4830.7440.0700.6550.078-0.1520.878-0.1471.0000.0770.4460.183-0.176-0.1690.365
Perceptions of corruption0.195-0.0030.1240.7370.0650.462-0.4210.0620.6850.0771.0000.039-0.1490.158-0.3330.515
Producer Price Inflation0.0960.5880.4790.0750.6500.187-0.1360.650-0.1420.4460.0391.0000.112-0.103-0.0130.396
Rank0.3790.1440.219-0.3890.175-0.552-0.0500.272-0.4650.183-0.1490.1121.000-0.997-0.563-0.069
Score0.380-0.144-0.2150.397-0.1730.5500.045-0.2700.469-0.1760.158-0.103-0.9971.0000.5540.077
Social support0.226-0.064-0.235-0.122-0.1250.1070.291-0.215-0.047-0.169-0.333-0.013-0.5630.5541.000-0.259
Year0.0000.2250.3680.6010.2720.568-0.5210.3270.5520.3650.5150.396-0.0690.077-0.2591.000

Missing values

2025-03-23T01:34:04.379137image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
A simple visualization of nullity by column.
2025-03-23T01:34:04.836406image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2025-03-23T01:34:05.320145image/svg+xmlMatplotlib v3.8.4, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

CountryYearRankScoreGDP per CapitaSocial supportHealthy life expectancy at birthFreedom to make life choicesGenerosityPerceptions of corruptionEnergy Consumer Price InflationFood Consumer Price InflationGDP deflator Index growth rateHeadline Consumer Price InflationOfficial Core Consumer Price InflationProducer Price InflationContinent
0Afghanistan20151533.57500.3198200.3028500.3033500.2341400.3651000.097190-4.250000-0.8400002.665090-0.6600000.219999NaNAsia
1Afghanistan20161543.36000.3822700.1103700.1734400.1643000.3126800.0711202.0700005.670000-2.4095094.3800005.192760NaNAsia
2Afghanistan20171413.79400.4014770.5815430.1807470.1061800.3118710.0611584.4400006.9400002.4040004.9760005.423228NaNAsia
3Afghanistan20181453.63200.3320000.5370000.2550000.0850000.1910000.0360001.474185-1.0459522.0712080.630000-0.126033NaNAsia
4Afghanistan20191543.20300.3500000.5170000.3610000.0000000.1580000.025000-2.4943593.7947706.5209282.302000NaNNaNAsia
5Afghanistan20201532.56690.3007060.47036752.5900000.396573-0.0964290.933687NaN5.8290055.3071205.443000NaNNaNAsia
6Afghanistan20211492.52307.6950000.46300052.4930000.382000-0.1020000.924000NaNNaN0.5245175.062000NaNNaNAsia
7Afghanistan20221462.40400.7580000.0000000.2890000.0000000.0890000.005000NaNNaN5.47507110.600000NaNNaNAsia
8Afghanistan20231371.85907.3240000.34100054.7120000.382000-0.0810000.847000NaNNaNNaNNaNNaNNaNAsia
9Albania2015954.95900.8786700.8043400.8132500.3573300.1427200.064130-0.5200004.3194890.5642781.910179-0.156957NaNEurope
CountryYearRankScoreGDP per CapitaSocial supportHealthy life expectancy at birthFreedom to make life choicesGenerosityPerceptions of corruptionEnergy Consumer Price InflationFood Consumer Price InflationGDP deflator Index growth rateHeadline Consumer Price InflationOfficial Core Consumer Price InflationProducer Price InflationContinent
1193Zambia20231283.98208.0740000.69400055.0320000.7910000.0980000.8180007.89349612.4391306.67177710.953000NaNNaNAfrica
1194Zimbabwe20151154.61000.2710001.0327600.3347500.2586100.1898700.080790-2.560000-3.3400000.607814-2.410000NaNNaNAfrica
1195Zimbabwe20161314.19300.3504100.7147800.1595000.2542900.1850300.085820-2.430000-3.2600002.160993-1.566000NaNNaNAfrica
1196Zimbabwe20171383.87500.3758471.0830960.1967640.3363840.1891430.095375-1.4100002.5000002.4429460.907000NaNNaNAfrica
1197Zimbabwe20181443.69200.3570001.0940000.2480000.4060000.1320000.0990000.99429014.5899435.22022710.610000NaNNaNAfrica
1198Zimbabwe20191463.66300.3660001.1140000.4330000.3610000.1510000.08900090.80807186.124371-4.035235255.292007NaNNaNAfrica
1199Zimbabwe20201513.29920.4255640.76309355.6172600.711458-0.0720640.810237306.431673601.020236568.971862557.210000NaNNaNAfrica
1200Zimbabwe20211483.14507.9430000.75000056.2010000.677000-0.0470000.82100069.820000105.800000113.29498198.546000NaNNaNAfrica
1201Zimbabwe20221442.99500.9470000.6900000.2700000.3290000.1060000.10500097.246550149.967034113.018434104.705171NaNNaNAfrica
1202Zimbabwe20231343.20407.6410000.69000054.0500000.654000-0.0460000.766000115.380648NaN812.247463105.401982NaNNaNAfrica